55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
import pandas
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from sklearn import tree
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import pydotplus
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from sklearn.tree import DecisionTreeClassifier
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import matplotlib.pyplot as plt
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import matplotlib.image as pltimg
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import joblib
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df = pandas.read_csv("treedata\\data.csv")
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#Map text values to number values
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d = {'toPlow' : 0, 'toWater' : 1, 'toSeed' : 2, 'toFertilize' : 3, 'toCut' : 4}
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df['Field'] = df['Field'].map(d)
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d = {'Night' : 0, 'Day' : 1}
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df['Day Time'] = df['Day Time'].map(d)
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d = {'Clear Sky' : 0, 'Cloudy' : 1, 'Rainy' : 2, 'Hail': 3}
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df['Weather'] = df['Weather'].map(d)
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d = {'Freezing' : 0, 'Cold' : 1, 'Mild': 2, 'Hot': 3}
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df['Temperature'] = df['Temperature'].map(d)
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d = {'Windless' : 0, 'Strong Wind' : 1, 'Gale': 2}
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df['Wind'] = df['Wind'].map(d)
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d = {'Low': 0, 'High': 1}
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df['Humidy'] = df['Humidy'].map(d)
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d = {'Wait' : 0, 'Make Action' : 1}
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df['Decision'] = df['Decision'].map(d)
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#Separate the feature columns from targert columns
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features = ['Field', 'Day Time', 'Weather', 'Temperature', 'Wind', 'Humidy']
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X = df[features]
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y = df['Decision']
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dtree = DecisionTreeClassifier()
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dtree = dtree.fit(X, y)
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joblib.dump(dtree, 'treedata\\DecisionTree.pkl')
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data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
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graph = pydotplus.graph_from_dot_data(data)
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graph.write_png('treedata\\mydecisiontree.png')
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img = pltimg.imread('treedata\\mydecisiontree.png')
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imgplot = plt.imshow(img)
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plt.show()
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#print(dtree.predict([[0, 1, 0, 0, 0, 1]])) |